如何使这段python代码简短高效

我对编程和python完全是新手。我在解决一个问题。我找到了解决办法，但似乎太慢了

    if n % 2 == 0 and n % 3 == 0 and\
       n % 4 == 0 and n % 5 == 0 and\
       n % 6 == 0 and n % 7 == 0 and\
       n % 8 == 0 and n % 9 == 0 and\
       n % 10 == 0 and n % 11 == 0 and\
       n % 12 == 0 and n % 13 == 0 and\
       n % 14 == 0 and n % 15 == 0 and\
       n % 16 == 0 and n % 17 == 0 and\
       n % 18 == 0 and n % 19 == 0 and\
       n % 20 == 0:

这是一段代码，用于检查

n

是否可被2到20之间的所有数字整除

我如何使它简短高效

if all(n % i == 0 for i in range(2, 21)):

all

接受一个iterable，如果它的所有元素都计算为

True

，则返回

True

，否则返回

False

。

n%i==0表示范围（2,21）内的i

部分返回一个包含19个

True

或

False

值的iterable，具体取决于

n

是否可被相应的

i

值所除。

内置将有所帮助

如果iterable的所有元素都为True（或者iterable为空），则返回True

---

在短小和高效之间有一个权衡

if all(n % i == 0 for i in range(2, 21)):

短路径是

if all（n%i==0表示范围（2,21）内的i）：

有效的方法是注意像

n%20==0

这样的事情也意味着

n%f==0

，其中

f

是20的任意因子。例如，您可以删除

n%2==0

。因此，您将得到更少的比较，从而运行得更快。在执行此操作时，您将注意到一个模式，并且您将注意到如果n%232792560==0，则整个语句将缩减为

！但这已经深深地嵌入了20，所以如果你需要一个不同的上限，就很难取消选择

因此，您可以看到，高效的方法并不容易阅读和维护。因此，选择一个最适合您的需求。
您需要一个条件，该条件在所有除法都给出零余数时计算为真。到目前为止提出的两个解决方案似乎没有做到这一点。我想你需要的条件是

if not any(n % i for i in range(2, 21)):

有一种更聪明的方法可以做到这一点。若
n
可被范围（1，21）中的每个整数整除，则它必须是这些整数的倍数

您可以使用GCD（最大公约数）逐步计算一组数字的LCM。您可以从
模块导入gcd函数，或者直接在代码中实现它

def gcd(a, b):
    ''' Greatest Common Divisor '''
    while b:
        a, b = b, a % b
    return a

def lcm(a, b):
    ''' Least Common Multiple '''
    return a * b // gcd(a, b)

# Compute the LCM of range(1, 21)
n = 2
for i in range(3, 21):
    n = lcm(n, i)

lcm20 = n
print('LCM =', lcm20)
#test 
for i in range(1, 21):
    print(i, lcm20 % i)

输出

LCM = 232792560
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0

ok

num = 2, loops = 8192
lcm_range_PM     [0.013914467999711633, 0.01393848999941838, 0.023966414999449626]
lcm_range_fast   [0.01656803699916054, 0.016577592001340236, 0.016578077998929075]
lcm_range_AS     [0.01738608899904648, 0.017602848000024096, 0.01770572900022671]
lcm_range_primes [0.0979132459997345, 0.09863009199943917, 0.10133290699923236]

num = 4, loops = 4096
lcm_range_fast   [0.01580070299860381, 0.01581421999981103, 0.016406731001552544]
lcm_range_AS     [0.020135083001150633, 0.021132826999746612, 0.021589830999801052]
lcm_range_PM     [0.02821666900126729, 0.029041511999821523, 0.036708851001094445]
lcm_range_primes [0.06287289499960025, 0.06381634699937422, 0.06406087200048205]

num = 8, loops = 2048
lcm_range_fast   [0.015360695999333984, 0.02138442599971313, 0.02630166100061615]
lcm_range_AS     [0.02104746699842508, 0.021742354998423252, 0.022648989999652258]
lcm_range_PM     [0.03499621999981173, 0.03546843599906424, 0.042924503999529406]
lcm_range_primes [0.03741390599861916, 0.03865244000007806, 0.03959638999913295]

num = 16, loops = 1024
lcm_range_fast   [0.015973221999956877, 0.01600381199932599, 0.01603960700049356]
lcm_range_AS     [0.023003745000096387, 0.023848425998949097, 0.024875303000953863]
lcm_range_primes [0.028887982000014745, 0.029422679001072538, 0.029940758000520873]
lcm_range_PM     [0.03780223299872887, 0.03925949299991771, 0.04462484900068375]

num = 32, loops = 512
lcm_range_fast   [0.018606906000059098, 0.02557359899947187, 0.03725786200084258]
lcm_range_primes [0.021675119000065024, 0.022790905999499955, 0.03934840099827852]
lcm_range_AS     [0.025330593998660333, 0.02545427500081132, 0.026093265998497372]
lcm_range_PM     [0.044320442000753246, 0.044836185001258855, 0.05193238799984101]

num = 64, loops = 256
lcm_range_primes [0.01650579099987226, 0.02443148000020301, 0.033489004999864846]
lcm_range_fast   [0.018367127000601613, 0.019002625000211992, 0.01955779200034158]
lcm_range_AS     [0.026258470001266687, 0.04113643799973943, 0.0436801750001905]
lcm_range_PM     [0.04854909000096086, 0.054864030998942326, 0.0797669980001956]

num = 128, loops = 128
lcm_range_primes [0.013294352000229992, 0.013383581999732996, 0.024317635999977938]
lcm_range_fast   [0.02098568399924261, 0.02108044199849246, 0.03272008299973095]
lcm_range_AS     [0.028861763999884715, 0.0399744570004259, 0.04660961700028565]
lcm_range_PM     [0.05302166500041494, 0.059346372001527925, 0.07757829000001948]

num = 256, loops = 64
lcm_range_primes [0.010487794999789912, 0.010514846000660327, 0.01055656300013652]
lcm_range_fast   [0.02619308099929185, 0.02637610199963092, 0.03755473099954543]
lcm_range_AS     [0.03422451699952944, 0.03513622399987071, 0.05206341099983547]
lcm_range_PM     [0.06851765200008231, 0.073690847000762, 0.07841700100107118]

num = 512, loops = 32
lcm_range_primes [0.009275872000216623, 0.009292663999076467, 0.009309271999882185]
lcm_range_fast   [0.03759837500001595, 0.03774761099884927, 0.0383951439998782]
lcm_range_AS     [0.04527828100071929, 0.046646228000099654, 0.0569303670017689]
lcm_range_PM     [0.11064135100059502, 0.12738902800083451, 0.13843623499997193]

num = 1024, loops = 16
lcm_range_primes [0.009248070000467123, 0.00931658900117327, 0.010279963000357384]
lcm_range_fast   [0.05642254200029129, 0.05663530499987246, 0.05796714499956579]
lcm_range_AS     [0.06509247900066839, 0.0652738099997805, 0.0658949799999391]
lcm_range_PM     [0.11376448099872505, 0.11652833600055601, 0.12083648199950403]

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

num = 30, loops = 1000
lcm_range_fast   [0.03275446999941778, 0.033530079999763984, 0.04002811799909978]
lcm_range_primes [0.04062690899991139, 0.040886697999667376, 0.04130547800014028]

num = 31, loops = 1000
lcm_range_fast   [0.03423191600086284, 0.039976395999474335, 0.04078094900069118]
lcm_range_primes [0.04053011599899037, 0.04140578700025799, 0.04566663300101936]

num = 32, loops = 1000
lcm_range_fast   [0.036124262000157614, 0.036700047998238006, 0.04392546200142533]
lcm_range_primes [0.042666604998885305, 0.04393434200028423, 0.05142524700022477]

num = 33, loops = 1000
lcm_range_fast   [0.03875456000059785, 0.03997290300139866, 0.044469664000644116]
lcm_range_primes [0.04280027899949346, 0.0437891679994209, 0.04381238600035431]

num = 34, loops = 1000
lcm_range_fast   [0.038203157999305404, 0.03937257799952931, 0.04531203700025799]
lcm_range_primes [0.043273317998682614, 0.043349457999283914, 0.04420187600044301]

num = 35, loops = 1000
lcm_range_fast   [0.04228670399970724, 0.04346491300020716, 0.047442203998798504]
lcm_range_primes [0.04332462999991549, 0.0433610400014004, 0.04525857199951133]

num = 36, loops = 1000
lcm_range_fast   [0.04175829099949624, 0.04217126499861479, 0.046840714998324984]
lcm_range_primes [0.04339772299863398, 0.04360795700085873, 0.04453475599984813]

num = 37, loops = 1000
lcm_range_fast   [0.04231068799890636, 0.04373836499871686, 0.05010528200000408]
lcm_range_primes [0.04371378700125206, 0.04463105400100176, 0.04481986299833807]

num = 38, loops = 1000
lcm_range_fast   [0.042841554000915494, 0.043649038998410106, 0.04868016199907288]
lcm_range_primes [0.04571479200058093, 0.04654245399979118, 0.04671720700025617]

num = 39, loops = 1000
lcm_range_fast   [0.04469198100014182, 0.04786454099848925, 0.05639159299971652]
lcm_range_primes [0.04572433999965142, 0.04583652600013011, 0.046649005000290344]

num = 40, loops = 1000
lcm_range_fast   [0.044788433999201516, 0.046223339000789565, 0.05302252199908253]
lcm_range_primes [0.045482261000870494, 0.04680115900009696, 0.046941823999077315]

num = 41, loops = 1000
lcm_range_fast   [0.04650144500010356, 0.04783133000091766, 0.05405569400136301]
lcm_range_primes [0.04678159699869866, 0.046870936999766855, 0.04726529199979268]

num = 42, loops = 1000
lcm_range_fast   [0.04772527699969942, 0.04824955299955036, 0.05483534199993301]
lcm_range_primes [0.0478546140002436, 0.048954233001495595, 0.04905354400034412]

num = 43, loops = 1000
lcm_range_primes [0.047872637000182294, 0.048093739000250935, 0.048502418998396024]
lcm_range_fast   [0.04906317900167778, 0.05292572700091114, 0.09274570399975346]

num = 44, loops = 1000
lcm_range_primes [0.049750300000596326, 0.050272532000235515, 0.05087747600009607]
lcm_range_fast   [0.050906279000628274, 0.05109869400075695, 0.05820328499976313]

num = 45, loops = 1000
lcm_range_primes [0.050158660000306554, 0.050309066000409075, 0.054478109999763547]
lcm_range_fast   [0.05236714599959669, 0.0539534259987704, 0.058996140000090236]

num = 46, loops = 1000
lcm_range_primes [0.049894845999006066, 0.0512076260001777, 0.051318084999365965]
lcm_range_fast   [0.05081920200063905, 0.051397655999608105, 0.05722950699964713]

num = 47, loops = 1000
lcm_range_primes [0.04971165599999949, 0.05024208400027419, 0.051092388999677496]
lcm_range_fast   [0.05388393700013694, 0.05502788499870803, 0.05994341699988581]

num = 48, loops = 1000
lcm_range_primes [0.0517014939996443, 0.05279760400117084, 0.052917389999493025]
lcm_range_fast   [0.05402479099939228, 0.055251746000067214, 0.06128628700025729]

num = 49, loops = 1000
lcm_range_primes [0.05412415899991174, 0.05474224499994307, 0.05610057699959725]
lcm_range_fast   [0.05757830900074623, 0.0590323519991216, 0.06310263200066402]

num = 50, loops = 1000
lcm_range_primes [0.054892387001018506, 0.05504404100065585, 0.05610281799999939]
lcm_range_fast   [0.0588886920013465, 0.0594741389995761, 0.06682244199873821]

num = 51, loops = 1000
lcm_range_primes [0.05582956999933231, 0.055921465000210446, 0.06004790299994056]
lcm_range_fast   [0.060586288000195054, 0.061715600999377784, 0.06733965300009004]

num = 52, loops = 1000
lcm_range_primes [0.0557458109997242, 0.05669860099988, 0.056761407999147195]
lcm_range_fast   [0.060323355999571504, 0.06177857100010442, 0.06778404599936039]

num = 53, loops = 1000
lcm_range_primes [0.05501838899908762, 0.05541463699955784, 0.0561610999993718]
lcm_range_fast   [0.06281833000139159, 0.06334177999997337, 0.06843207200108736]

num = 54, loops = 1000
lcm_range_primes [0.057314272000439814, 0.059501444000488846, 0.060004871998899034]
lcm_range_fast   [0.06634221600143064, 0.06662889200015343, 0.07153233899953193]

num = 55, loops = 1000
lcm_range_primes [0.05790564500057371, 0.05824322199987364, 0.05863306900027965]
lcm_range_fast   [0.06693624800027465, 0.06784769100158883, 0.07562533499913116]

num = 56, loops = 1000
lcm_range_primes [0.057219010001063, 0.05858367799919506, 0.06246676000046136]
lcm_range_fast   [0.06854197999928147, 0.06999059400004626, 0.07505119899906276]

num = 57, loops = 1000
lcm_range_primes [0.05746709300001385, 0.0587476679993415, 0.0606189070003893]
lcm_range_fast   [0.07094627400147147, 0.07241532700027165, 0.07868066799892404]

num = 58, loops = 1000
lcm_range_primes [0.0576490580006066, 0.058481812999161775, 0.05857339500107628]
lcm_range_fast   [0.07127979200049595, 0.07549924399972952, 0.07849203499972646]

num = 59, loops = 1000
lcm_range_primes [0.057503377998727956, 0.058632499998566345, 0.060360438999850885]
lcm_range_fast   [0.07332589399993594, 0.07625177999943844, 0.08087236799838138]

现在，要测试任何数字
n
是否可被所有数字整除，可以执行以下操作

n % lcm20 == 0

或者将常量硬编码到脚本中：

# 232792560 is the LCM of 1..20
n % 232792560 == 0


正如安东·舍伍德（Anton Sherwood）在中指出的那样，我们只需获取范围上半部分的LCM，就可以加快找到所需LCM的过程。这是因为范围下半部分的每个数字都是范围上半部分数字的除数

通过排列GCD和LCM计算，而不是调用函数来执行这些操作，我们可以进一步提高速度。Python函数调用明显比C函数调用慢，这是由于涉及到额外的开销

雅克提到了另一种寻找所需LCM的方法：计算范围内的素数幂的乘积。如果范围足够大（大约40个左右），这是相当快的，但是对于较小的数字，简单的LCM循环速度更快

下面是一些比较各种方法速度的
timeit
代码。这个脚本在Python2和3上运行，我已经在Python2.6和Python3.6上对它进行了测试。它使用Robert William Hanks的素数列表函数来实现Yakk的建议。我稍微修改了Robert的代码，使其与Python 3兼容。我想可能有一种更有效的方法来寻找主要力量；如果是的话，我想看看。：）

我前面提到，
分数
模块中有一个GCD函数。我用它做了一些时间测试，但它明显比我的代码慢。这大概是因为它对参数进行错误检查

#!/usr/bin/env python3

''' Least Common Multiple of the numbers in range(1, m)

    Speed tests

    Written by PM 2Ring 2016.08.04
'''


from __future__ import print_function
from timeit import Timer
#from fractions import gcd

def gcd(a, b):
    ''' Greatest Common Divisor '''
    while b:
        a, b = b, a % b
    return a

def lcm(a, b):
    ''' Least Common Multiple '''
    return a * b // gcd(a, b)

def primes(n):
    ''' Returns a list of primes < n '''
    # By Robert William Hanks, from https://stackoverflow.com/a/3035188/4014959
    sieve = [True] * (n//2)
    for i in range(3, int(n ** 0.5) + 1, 2):
        if sieve[i//2]:
            sieve[i*i//2::i] = [False] * ((n - i*i - 1) // (2*i) + 1)
    return [2] + [2*i + 1 for i in range(1, n//2) if sieve[i]]

def lcm_range_PM(m):
    ''' The LCM of range(1, m) '''
    n = 1
    for i in range(2, m):
        n = lcm(n, i)
    return n

def lcm_range_AS(m):
    ''' The LCM of range(1, m) '''
    n = m // 2
    for i in range(n + 1, m):
        n = lcm(n, i)
    return n

def lcm_range_fast(m):
    ''' The LCM of range(1, m) '''
    n = m // 2
    for i in range(n + 1, m):
        a, b = n, i
        while b:
            a, b = b, a % b
        n = n * i // a
    return n

def lcm_range_primes(m):
    n = 1
    for p in primes(m):
        a = p
        while a < m:
            a *= p
        n *= a // p
    return n

funcs = (
    lcm_range_PM,
    lcm_range_AS,
    lcm_range_fast,
    lcm_range_primes
)

def verify(hi):
    ''' Verify that all the functions give the same result '''
    for i in range(2, hi + 1):
        a = [func(i) for func in funcs]
        a0 = a[0]
        assert all(u == a0 for u in a[1:]), (i, a)
    print('ok')

def time_test(loops, reps):
    ''' Print timing stats for all the functions '''
    timings = []
    for func in funcs:
        fname = func.__name__
        setup = 'from __main__ import num, ' + fname
        cmd = fname + '(num)'
        t = Timer(cmd, setup)
        result = t.repeat(reps, loops)
        result.sort()
        timings.append((result, fname))

    timings.sort()
    for result, fname in timings:
        print('{0:16} {1}'.format(fname, result))

verify(500)

reps = 3
loops = 8192
num = 2
for _ in range(10): 
    print('\nnum = {0}, loops = {1}'.format(num, loops))
    time_test(loops, reps)
    num *= 2
    loops //= 2

print('\n' + '- ' * 40)

funcs = (
    lcm_range_fast,
    lcm_range_primes
)

loops = 1000
for num in range(30, 60):
    print('\nnum = {0}, loops = {1}'.format(num, loops))
    time_test(loops, reps)

这个计时信息是使用Python 3.6生成的，它运行在Linux的Debian衍生物上，运行在一台古老的2GHz Pentium IV机器上。
这只是一个数学技巧，
使用类似于
n%”的LCM（1,2，…，20）=0
可以编码为：

if n % 232792560 == 0:
    #do whatever you want

对于多样性，使用循环的方式是

test = True
for modulus in range(2, 21):
    if n % modulus != 0:
        test = False
        break
if test:
    # Do stuff

如果您对
for
-
else
感到满意，您可以通过

for modulus in range(2, 21):
    if n % modulus != 0:
        break
else:
    # Do stuff

尽管这种模式可能非常不寻常，以至于你不想使用它

另一个选项是编写辅助函数

def is_divisible_by_integers_up_to(n, bound):
    for modulus in range(2, bound + 1):
        if n % modulus != 0:
            return False
    return True

if is_divisible_by_integers_up_to(n, 20):
    # Do stuff

然而，这个特殊的例子非常简单，使用其他答案中描述的生成器表达式执行
all
是最好的方法。
我自己也是一个非常轻量级的python用户，对所有问题都不了解。这些解决方案非常酷（可能比我即将发布的解决方案更有效）.但如果你想看到另一种方法，这里有另一种选择：

def IsDivUpTo20(n):
   for i in range(2, 21):
      if n % i != 0:
         return False
   return True

就这样说吧

if IsDivUpTo20(50):
   #what to do if it is divisible
else:
   #what to do if it isn't
#for the example of 50, it'll be false and jump to the else part, but you can put any number of variable in there

从功能上讲，它的工作方式与“all”几乎相同，但如果您不习惯这种奇特的语法和内置功能，那么这一点就更直观了

*注意：我使用的是Python 3，而不是Python 2.7，因为问题是有标记的。我很确定这在该版本中有效，但如果没有，请有人纠正我。
类似于前面的答案：

import operator
x = 232792560
if reduce(operator.__and__, [x % n == 0 for n in xrange(2, 21, 2)]):
    print("ok")

上述许多代码示例较短，但（可能）效率不够：

n%2 == 0 =>
    n%4 6 8... ==0
n%3 == 0 =>
    n%3 6 9... ==0

我们只能使用素数在以下范围内进行检查：

if all(n % i == 0 for i in [2,3,5,7,11,13,17,19])

此外，如果n将all从2除以20，它将LCM从2除以20。
我不知道回答你自己的问题是否正确

因为我需要检查。一个数字是否可以被1到20的数字整除。所以检查需要很长时间。但如果我能把检查表缩短，那么它会很有效

if all(n % i == 0 for i in range(2, 21)):

比如，如果一个数字可以被
18
整除，那么它也应该被
2
3
6
和
9
整除

if all(n % i == 0 for i in [7,11,13,16,17,18,19,20]):

    # some code

对于
14
15
和
12
这样想

14
：